13hannes11/UU_NCML_Project
1
0
Fork 0
mirror of https://github.com/13hannes11/UU_NCML_Project.git synced 2024-09-03 20:50:59 +02:00
Code Issues Projects Releases Wiki Activity

Slight changes in visualization

Browse Source
This commit is contained in:
Deepthi Pathare
2021-05-21 10:11:07 +02:00
parent f3fecfdc12
commit 09daf22fe3
1 changed files with 5 additions and 19 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
voting_lib/voting_analysis.py
View File

@@ -64,10 +64,7 @@ def predict(model, data, grid_h, grid_w, comparison_data=pd.DataFrame()):
# plotting parties
plot_parties(party_pos, randomize_positions=False, new_plot=False)
plt.xticks(np.arange(0, grid_w+1, 1.0))
plt.yticks(np.arange(0, grid_h+1, 1.0))
plt.grid(True)
plt.title(f'Learning Radius: {model.learning_radius}, Grid Size: {grid_w}')
plt.title('Node distance plot with parties')
# plotting party distances in output space
part_distance_out = calc_party_distances(party_pos)
@@ -91,13 +88,6 @@ def predict(model, data, grid_h, grid_w, comparison_data=pd.DataFrame()):
plt.title(f'Normalized Distance Squared Error, with MSE={np.nanmean(err.to_numpy()):.2f}')
plt.show()
# # plotting party distances in input space (TODO discard)
# party_pos_out = calc_party_pos(X, party_affiliation)
# part_distance_in = calc_party_distances(party_pos_out)
# plot_party_distances(part_distance_in)
# plt.show()
def iter_neighbours(weights, hexagon=False):
_, grid_height, grid_width = weights.shape
@@ -206,6 +196,7 @@ def calc_party_pos(members_of_parliament, party_affiliation):
party_pos /= party_count
return pd.DataFrame(data=party_pos, index=party_index_mapping)
def plot_parties(parties, randomize_positions=False, new_plot=True):
party_index_mapping = parties.index
@@ -220,17 +211,12 @@ def plot_parties(parties, randomize_positions=False, new_plot=True):
xs_disp = parties[0].to_numpy()
ys_disp = parties[1].to_numpy()
# party_colors=np.array(range(len(party_index_mapping)))
# plt.scatter(xs_disp, ys_disp, c=party_colors,cmap=plt.cm.RdYlGn, zorder=2)
# offset = 0.01
# for x,y, party in zip(xs_disp, ys_disp, party_index_mapping):
# plt.text(x + offset, y + offset, party)
for i, party in enumerate(party_index_mapping):
print("Party ", party, " x = ", xs_disp[i], "y = ", ys_disp[i])
plt.scatter(xs_disp[i], ys_disp[i], label=party, zorder=2)
plt.legend(title='Parties')
plt.legend(title='Parties', bbox_to_anchor=(1.3, 1), loc='upper left')
def calc_party_distances(parties):
distances = np.zeros((parties.shape[0], parties.shape[0]))
for i, (_, left_party) in enumerate(parties.iterrows()):
@@ -241,7 +227,7 @@ def calc_party_distances(parties):
return pd.DataFrame(data=distances, index=party_index_mapping, columns=party_index_mapping)
def plot_party_distances(distances):
fig = plt.figure()
plt.figure()
ax = plt.gca()
ax.tick_params(axis="x", bottom=False, top=True, labelbottom=False, labeltop=True)
sn.heatmap(distances, cmap='Oranges', annot=True)